Regarding the imbalancement problem, it may be considered a serious problem for model learning: in fact, a learner can analyze the data and cleverly decide that the best thing to do is to always predict the majority class without performing any analysis of the features. In the static settings there are two approaches to rebalance a dataset: under-sampling the majority class removing random records or oversampling the minority class replicating random existing records. One of the most powerful techniques is Smote [1]}. It over-samples the minority class at a certain percentage by creating synthetic samples. For each minority class sample, Smote finds its \(k\)  nearest neighbours from the minority class samples, it randomly chooses one from them and uses it to create synthetic samples.
